English(EN) Hurdle-RMIL: Addressing Zero Inflation and Long-Tailed Imbalance in Infrared Rainfall Retrieval

新AI方法改进稀有、强降雨检测

作者 PulseAugur 编辑部 · [1 个来源] · 2026-09-14 04:00

研究人员开发了Hurdle-RMIL，一种改进卫星数据红外雨量反演精度的新方法。该方法专门解决了数据不平衡的挑战，即稀有但强烈的降雨事件常常被低估。通过将零膨胀与降雨数据的长尾分布分开，Hurdle-RMIL在不显著影响低降雨率精度的前提下，增强了强降雨的检测能力。在中国进行的测试表明，Hurdle-RMIL的性能优于传统学习方法，尤其是在极端降雨事件方面，其公平威胁得分更高。 AI

影响通过改进对极端天气事件的检测能力，增强了AI在关键环境监测方面的能力。

排序理由详细介绍一种新的基于AI的雨量反演方法的学术论文。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.LG 阅读 →

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报道来源 [1]

arXiv cs.LG TIER_1 English(EN) · Fangjian Zhang, Xiaoyong Zhuge, Wenlan Wang, Haixia Xiao, Yuying Zhu, Siyang Cheng, Ali Mamtimin · 2026-09-14 04:00

Hurdle-RMIL：解决红外降水反演中的零点膨胀和长尾不平衡问题

arXiv:2510.20486v2 Announce Type: replace Abstract: Imbalanced labels can cause frequent samples to dominate AI-based quantitative remote sensing, degrading rare-event retrieval. In rain-rate retrieval based on satellite infrared brightness temperatures, this imbalance leads to s…

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Hurdle-RMIL：解决红外降水反演中的零点膨胀和长尾不平衡问题

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